EQ Sight — JupyterLab

The JupyterLab view, accessible via the Erlenmeyer flask icon in the sidebar, provides an interactive Python environment for advanced analysis of power quality data. JupyterLab is pre-installed on EQ Gateways along with equser, an open-source Python package for working with EQ Wave data.

Overview

JupyterLab provides an interactive analysis environment for users who need to:

• Perform custom calculations on waveform data
• Create specialized visualizations
• Develop automated analysis workflows
• Export data in custom formats

Getting Started

When you open the JupyterLab tab:

1. A new notebook session starts automatically
2. The equser package is pre-installed and ready to use
3. Sample notebooks in tutorials/, analysis/, and tools/ demonstrate common analysis patterns

You can also install equser on any computer with pip install equser to work with exported data or connect to a gateway remotely.

Working with CPOW Waveform Data

Load continuous point-on-wave (CPOW) parquet files directly from the gateway’s storage:

from equser.data import load_cpow_scaled

# Load a CPOW parquet file (32 kHz, 7 channels)
data = load_cpow_scaled('/var/lib/eq/data/cpow/20250615_120000.parquet')

# Scaled voltage and current arrays are ready to use
print(f"Phase A voltage range: {data['VA'].min():.1f} to {data['VA'].max():.1f} V")
print(f"Start time: {data['start_time']}")
print(f"Sample rate: {data['sample_rate']} Hz")
print(f"Samples: {len(data['VA']):,}")

The load_cpow_scaled() function automatically handles raw int32-to-float scaling using the vscale/iscale metadata embedded in each parquet file.

Querying Data via the REST API

The gateway exposes a REST API at port 8080. See the API Reference for endpoint details.

Power Monitoring Data (Arrow IPC)

from equser.api import api_get_arrow

# Fetch recent PMon data for a device
table = api_get_arrow('/api/v1/devices/wave-001/pmon/data', params={
    'start_time': '2025-06-15T12:00:00Z',
    'end_time': '2025-06-15T13:00:00Z',
})

# table is a pyarrow.Table; convert to pandas for analysis
df = table.to_pandas()
print(df.columns.tolist())

SQL Queries

from equser.api import api_post_sql

# Query aggregated power data via SQL
results = api_post_sql(
    "SELECT timestamp, vrms_a, vrms_b, vrms_c FROM pmon ORDER BY timestamp DESC",
    limit=100
)

Live Streaming via WebSocket

from equser.api import connect_spectral_ws

# Stream real-time spectral data
for frame in connect_spectral_ws(device_id='wave-001', phase='va', fft_size=4096):
    print(f"Frequencies: {len(frame.get('magnitudes', []))} bins")
    break  # Remove to stream continuously

Plotting Tools

The equser package includes plotting utilities for common visualizations:

from equser.plotting import PowerMonitorPlotter, WaveformPlotter

Waveform analysis helpers such as find_zero_crossings(), extract_complete_cycles(), and plot_extracted_cycles() are available for detailed cycle-level inspection.

Saving Work

Your notebooks are automatically saved to your user workspace. You can also:

• Download notebooks to your local computer
• Export results as CSV, PNG, or PDF

Resources

• Sample Notebooks: Browse the tutorials/, analysis/, and tools/ directories in the file browser
• API Documentation: Built-in help via help(equser)
• equser Package: See from equser import pmon, plotting, analysis, data, api for available modules

Performance Notes

JupyterLab runs on the gateway alongside EQ Watch and EQ Sight. For large data analyses:

• Use time filters to limit data volume
• Consider downsampling for trend analysis
• Export large datasets for processing on dedicated workstations